There are many applications, for example gaming, human-computer interaction, security, telepresence or health-care, which require robust automatic image labeling. For example, the image labeling may be to label human or animal body parts for pose estimations, to label anatomical structures for medical image analysis and for other purposes.
For example a gaming application may require real-time identification and tracking of body parts for one or more players. The players will be in many different poses as they play the game and may have a wide range of heights and/or body shapes. In another example a doctor may wish to carry out rapid identification of anatomical structures on a medical image. However, differences in the angle or format of an image and the differences in size and shape of various anatomical structures between different subjects make this difficult.
Conventional intensity cameras may be used to obtain images for labeling. Recently, real-time depth cameras have been used in the process of human body pose tracking.
Existing image labeling systems may be slow to run and require a laborious training process with large sets of training data. For example, in order to recognize a wide range of human body shapes and sizes in different poses.
The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known image labeling systems.